Contrastive Learning

Contrastive Learning is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart.

It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering.

(Image credit: Schroff et al. 2015)

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1471–1480 of 6661 papers

Title	Date	Tasks	Status	Hype
SAPNet: Segmentation-Aware Progressive Network for Perceptual Contrastive Deraining	Nov 17, 2021	Contrastive LearningImage Restoration	CodeCode Available	1
Improving Word Translation via Two-Stage Contrastive Learning	Nov 16, 2021	Bilingual Lexicon InductionContrastive Learning	CodeCode Available	1
Probabilistic Contrastive Learning for Domain Adaptation	Nov 11, 2021	Contrastive LearningDomain Adaptation	CodeCode Available	1
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos	Nov 11, 2021	Contrastive LearningImage Segmentation	CodeCode Available	1
On Representation Knowledge Distillation for Graph Neural Networks	Nov 9, 2021	Contrastive LearningKnowledge Distillation	CodeCode Available	1
TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning	Nov 7, 2021	Contrastive LearningNatural Language Understanding	CodeCode Available	1
Hard Negative Sampling via Regularized Optimal Transport for Contrastive Representation Learning	Nov 4, 2021	Contrastive LearningRepresentation Learning	CodeCode Available	1
Callee: Recovering Call Graphs for Binaries with Transfer and Contrastive Learning	Nov 2, 2021	Contrastive LearningQuestion Answering	CodeCode Available	1
Improving Contrastive Learning on Imbalanced Seed Data via Open-World Sampling	Nov 1, 2021	AttributeContrastive Learning	CodeCode Available	1
When Does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?	Nov 1, 2021	Adversarial RobustnessContrastive Learning	CodeCode Available	1

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All datasets imagenet-1k 10,000 People - Human Pose Recognition Data CIFAR-10 STL-10

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet50	ImageNet Top-1 Accuracy	73.6	—	Unverified
2	ResNet50	ImageNet Top-1 Accuracy	73	—	Unverified
3	ResNet50	ImageNet Top-1 Accuracy	71.1	—	Unverified
4	ResNet50	ImageNet Top-1 Accuracy	69.3	—	Unverified
5	ResNet50 (v2)	ImageNet Top-1 Accuracy	67.6	—	Unverified
6	ResNet50 (v2)	ImageNet Top-1 Accuracy	63.8	—	Unverified
7	ResNet50	ImageNet Top-1 Accuracy	63.6	—	Unverified
8	ResNet50	ImageNet Top-1 Accuracy	61.5	—	Unverified
9	ResNet50	ImageNet Top-1 Accuracy	61.5	—	Unverified
10	ResNet50 (4×)	ImageNet Top-1 Accuracy	61.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	1	0..5sec	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	IPCL (ResNet18)	Accuracy (Top-1)	84.77	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	IPCL (ResNet18)	Accuracy (Top-1)	85.55	—	Unverified